We train the agents using neural fitted Q learning [1]}, with an Adam optimizer [2]} and \(Q_t(\lambda \) ) where \(\lambda =0.9\) . The Q values are updated using temporal difference (TD) error where the bootstrapped \(Q_t(\lambda \) ) is defined as follows:
\(Q_t(\lambda ) = (1-\lambda ) \sum _{n=1}^{\infty } \lambda ^{n-1} Q_t^{(n)}\) 
